Businesses across every industry are rolling out AI services this year. For Microsoft, Oracle, Perplexity, Snap and hundreds of other leading companies, using the NVIDIA AI inference platform - a full stack comprising world-class silicon, systems and software - is the key to delivering high-throughput and low-latency inference and enabling great user experiences while lowering cost.
NVIDIA's advancements in inference software optimization and the NVIDIA Hopper platform are helping industries serve the latest generative AI models, delivering excellent user experiences while optimizing total cost of ownership. The Hopper platform also helps deliver up to 15x more energy efficiency for inference workloads compared to previous generations.
AI inference is notoriously difficult, as it requires many steps to strike the right balance between throughput and user experience.
But the underlying goal is simple: generate more tokens at a lower cost. Tokens represent words in a large language model (LLM) system - and with AI inference services typically charging for every million tokens generated, this goal offers the most visible return on AI investments and energy used per task.
Full-stack software optimization offers the key to improving AI inference performance and achieving this goal.
Cost-Effective User Throughput Businesses are often challenged with balancing the performance and costs of inference workloads. While some customers or use cases may work with an out-of-the-box or hosted model, others may require customization. NVIDIA technologies simplify model deployment while optimizing cost and performance for AI inference workloads. In addition, customers can experience flexibility and customizability with the models they choose to deploy.
NVIDIA NIM microservices, NVIDIA Triton Inference Server and the NVIDIA TensorRT library are among the inference solutions NVIDIA offers to suit users' needs:
NVIDIA NIM inference microservices are prepackaged and performance-optimized for rapidly deploying AI foundation models on any infrastructure - cloud, data centers, edge or workstations.
NVIDIA Triton Inference Server, one of the company's most popular open-source projects, allows users to package and serve any model regardless of the AI framework it was trained on.
NVIDIA TensorRT is a high-performance deep learning inference library that includes runtime and model optimizations to deliver low-latency and high-throughput inference for production applications.
Available in all major cloud marketplaces, the NVIDIA AI Enterprise software platform includes all these solutions and provides enterprise-grade support, stability, manageability and security.
With the framework-agnostic NVIDIA AI inference platform, companies save on productivity, development, and infrastructure and setup costs. Using NVIDIA technologies can also boost business revenue by helping companies avoid downtime and fraudulent transactions, increase e-commerce shopping conversion rates and generate new, AI-powered revenue streams.
Cloud-Based LLM Inference To ease LLM deployment, NVIDIA has collaborated closely with every major cloud service provider to ensure that the NVIDIA inference platform can be seamlessly deployed in the cloud with minimal or no code required. NVIDIA NIM is integrated with cloud-native services such as:
Amazon SageMaker AI, Amazon Bedrock Marketplace, Amazon Elastic Kubernetes Service
Google Cloud's Vertex AI, Google Kubernetes Engine
Microsoft Azure AI Foundry coming soon, Azure Kubernetes Service
Oracle Cloud Infrastructure's data science tools, Oracle Cloud Infrastructure Kubernetes Engine
Plus, for customized inference deployments, NVIDIA Triton Inference Server is deeply integrated into all major cloud service providers.
For example, using the OCI Data Science platform, deploying NVIDIA Triton is as simple as turning on a switch in the command line arguments during model deployment, which instantly launches an NVIDIA Triton inference endpoint.
Similarly, with Azure Machine Learning, users can deploy NVIDIA Triton either with no-code deployment through the Azure Machine Learning Studio or full-code deployment with Azure Machine Learning CLI. AWS provides one-click deployment for NVIDIA NIM from SageMaker Marketplace and Google Cloud provides a one-click deployment option on Google Kubernetes Engine (GKE). Google Cloud provides a one-click deployment option on Google Kubernetes Engine, while AWS offers NVIDIA Triton on its AWS Deep Learning containers.
The NVIDIA AI inference platform also uses popular communication methods for delivering AI predictions, automatically adjusting to accommodate the growing and changing needs of users within a cloud-based infrastructure.
From accelerating LLMs to enhancing creative workflows and transforming agreement management, NVIDIA's AI inference platform is driving real-world impact across industries. Learn how collaboration and innovation are enabling the organizations below to achieve new levels of efficiency and scalability.
Serving 400 Million Search Queries Monthly With Perplexity AI Perplexity AI, an AI-powered search engine, handles over 435 million monthly queries. Each query represents multiple AI inference requests. To meet this demand, the Perplexity AI team turned to NVIDIA H100 GPUs, Triton Inference Server and TensorRT-LLM.
Supporting over 20 AI models, including Llama 3 variations like 8B and 70B, Perplexity processes diverse tasks such as search, summarization and question-answering. By using smaller classifier models to route tasks to GPU pods, managed by NVIDIA Triton, the company delivers cost-efficient, responsive service under strict service level agreements.
Through model parallelism, which splits LLMs across GPUs, Perplexity achieved a threefold cost reduction while maintaining low latency and high accurac
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Businesses across every industry are rolling out AI services this year. For Microsoft, Oracle, Perplexity, Snap and hundreds of other leading companies, using the NVIDIA AI inference platform - a full stack comprising world-class silicon, systems and software - is the key to delivering high-throughput and low-latency inference and enabling great user experiences while lowering cost.
